| Predictions.GP {COST} | R Documentation |
new location prediction by Gaussian process method
Description
new location prediction by Gaussian process method, and the marginal mean and variance of the new location is estimated by neighboring information; it gives 0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each new location, at time n, conditional on observed locations at time n-1 and n; both point and interval predictions are provided
Usage
Predictions.GP(par,Y,s.ob,s.new,isotropic)
Arguments
par |
parameters in the copula function |
Y |
observed data |
s.ob |
coordinates of observed locations |
s.new |
coordinates of new locations |
isotropic |
indicator, True for isotropic correlation matrix, False for anisotropic correlation matrix, and we usually choose False for flexibility |
Value
0.025-, 0.975- and 0.5-th conditional quantiles of the conditional distribution for each new location, at time n
Author(s)
Yanlin Tang and Huixia Judy Wang
References
Yanlin Tang, Huixia Judy Wang, Ying Sun, Amanda Hering. Copula-based semiparametric models for spatio-temporal data.